Background. The success of genome sequencing projects makes it feasible to identify the gene and protein components that make up an organism. What remains difficult, however, is to measure or predict how these components are organized into functional units – protein complexes, pathways, organelles, and larger biological systems. Even the emerging field of synthetic biology, which aims to re-engineer cells for specific tasks, requires pathway maps as a framework for design.
My research shows how networks of genes and proteins achieve their function. The biological significance of this work has been to predict gene function based on network context, to identify functionally distinct modules representing protein complexes and pathways, and to establish links between genes and disease. Hundreds of our computational predictions have been validated experimentally.
Biological networks encompass many types of interactions that coexist simultaneously in the cell. Transient protein-protein and protein-DNA interactions occur in signal transduction and gene regulatory networks, often described as the wiring diagram of a cell. Networks formed by stable physical interactions between proteins define a cell’s structural components. Basic biochemical processes are defined by enzyme-substrate relationships in metabolic networks. Beyond these networks composed of physical interactions, epistatic or genetic interaction networks are defined by logical relationships: whether genes in a regulatory network are upstream or downstream of each other, for example, or located in parallel pathway branches that provide robust back-up systems. My group’s work is in three major areas.
Systems biology. We map biological pathways as part of interdisciplinary teams. We generated a map of protein-protein interactions in Drosophila, the first proteome-scale map for any multi-cellular organism. We developed statistical methods that are now widely used to assess the specificity and sensitivity of interactions identified by high-throughput biological technologies. Our group continues to contribute to ongoing experimental mapping campaigns.
We also develop new algorithms to analyze biological networks. Key innovations have been new algorithms for analyzing genetic epistasis networks, algorithms for joint analysis of physical and genetic interactions, and new methods for predicting disease associations based on metabolic networks. Our analysis discovered previously unknown components of the phagosome, an organelle responsible for internalizing and digesting microbial pathogens. We also successfully predicted how multiple hits to partially redundant DNA repair pathways are lethal in yeast; mutations in the corresponding human genes are implicated in cancer.
Synthetic biology. We develop computational methods for synthetic biology in order to design new biological systems with desired properties. Our lab provides unique web-based resources for team-based editing of DNA sequences from the resolution of a single basepair to an entire chromosome or genome. We are applying these methods to design and build a yeast cell with fully synthetic DNA and to identify which residues of pleiotropic proteins are responsible for each specific function.
Human genetics and disease. Our group develops and applies methods to analyze data from genome-wide association studies, including genotype data for common variants and exome and whole genome sequence data for rare variants. We also study infectious disease and host-pathogen interactions.